Computer vision may include object recognition, object categorization, object class detection, image classification, etc. Object recognition may describe finding a particular object (e.g., a handbag of a particular make, a face of a particular person, etc.). Object categorization and object class detection may describe finding objects that belong in a particular category or class (e.g., faces, shoes, cars, etc.). Image classification may describe assigning an entire image to a particular category or class (e.g., location recognition, texture classification, etc.). Computerized object recognition, detection, and/or classification using images is challenging because objects in the real world vary greatly in visual appearance. For instance, objects associated with a single label (e.g., cat, dog, car, house, etc.) exhibit diversity in color, shape, size, viewpoint, lighting, etc.
Some current object detection, recognition, and/or classification methods include training classifiers based on supervised, or labeled, data. Such methods are not scalable. Others of the current object detection, recognition, and/or classification methods leverage localized image features (e.g., Histogram of Oriented Gradients (HOG)) to learn common-sense knowledge (e.g., eye is part of a person) or specific sub-labels of generic labels (e.g., a generic label of horse includes sub-labels of brown horse, riding horse, etc.). However, using localized image features (e.g., HOG) is computationally intensive. Accordingly, current techniques for object detection, recognition, and/or classification are not scalable and are computationally intensive.